Abstract

A class of stochastic differential equations given by , , , are investigated. Upon making some suitable assumptions, the existence and uniqueness of solution for the equations are obtained. Moreover, the existence and uniqueness of solution
for stochastic Lorenz system, which is illustrated by example, are in good agreement with the theoretical analysis.

1. Introduction

Stochastic differential equations (SDEs) play an important role in characterizing many social, physical, biological, and engineering problems. They are important from the viewpoint of applications since they incorporate (natural) randomness into the mathematical description of the phenomena and provide a more accurate description of it. Therefore, the theory of SDEs has developed quickly, the investigation for SDEs has attracted considerable attention of researchers, and many qualitative theories of SDEs have been obtained (see [1–9] and the references therein).

The existence and uniqueness of solution are among the most basic and key topics in qualitative theory of SDEs. In the last two decades, the existence and uniqueness of solution for SDEs have been considered in many publications such as [10–14] and the references therein. Especially, Mao had investigated the stochastic differential equations:
on the closed interval , , in his book [14], and obtained that if Lipschitz condition
and linear growth condition:
hold, then (1) had a unique solution satisfying . Furthermore, Mao [14] also discussed stochastic functional differential equations with finite delay:
where could be considered as a -value stochastic process. The initial value is an -measurable -value random variable such that . For (4), if uniform Lipschitz condition
for any , , and linear growth condition
are satisfied, then (4) had a unique solution ; moreover, .

To the best of our knowledge, most of the results on existence theory for SDEs focused on the case of the coefficient satisfying linear growth condition; however, the results on existence theory for SDEs without linear growth condition were discussed seldom. In this paper, without linear growth condition, some new criteria ensuring the existence and uniqueness of solutions for a class of SDEs are firstly established. These criteria improve, complement a number of existing results, and handle some cases not covered by known criteria. The results obtained in this paper not only improve the previous conclusion in the case of linear growth condition, but also can be applied in the existence and uniqueness of solution for the stochastic Lorenz system for weather forecasting, some other systems, and so on.

The rest of this paper is organized as follows. In Section 2, some relating notations and preliminary facts are introduced. Section 3 obtains the existence and uniqueness of solution for a class of SDEs without linear growth condition. In Section 4, an interesting examples is given to show the effectiveness of our results.

2. Preliminaries

This section is concerned with some notations and preliminary results which are used in what follows.

In this paper, we adopt the symbols as follow: denotes the usual -dimensional Euclidean space, denotes norm in . If is a vector or a matrix, its transpose is denoted by ; if is a matrix, its trace norm is represented by . Let be a complete probability space with a filtration satisfying the usual conditions (i.e., it is increasing and right continuous). is independent of the -field generated by and contains all -null sets. is a given -dimensional standard Brownian motion.

Consider -dimensional stochastic differential equations:
where , is independent of , and satisfies is an -dimensional Wiener process, , is a matrix. Our purpose is to find the solution for (7). Hence, we will show the existence, uniqueness theorem and the properties of solution for (7) in the next section. Moreover, to illustrate the effectiveness of our results, we prove the existence and uniqueness of solution for the stochastic Lorenz system.

To guarantee the existence and uniqueness of solution for (7), the following conditions, instead of Lipschitz and linear growth conditions, are described.

satisfies the Lipschitz condition; moreover,

satisfies the Lipschitz condition and the linear growth condition:
where denotes the inner product of and , , and are constants.

3. The Existence and Uniqueness Theorem

In this section, we start to study the existence and uniqueness of solution for (7) without linear growth condition. To complete our main results, we need to prepare several lemmas which will be utilized in the sequel.

Lemma 1. Let and be satisfied. Setting with
and , and then the modified stochastic differential equations:
possesses a continuous almost sure unique measurable solution process.

Proof. As the truncation function , remains differentiable and its derivative is both continuous and has a compact support. Hence is bounded and satisfies linear growth condition as well as Lipschitz condition. According to , satisfies linear growth condition and Lipschitz condition. Therefore, the assertion follows by the usual existence and uniqueness theorem (see Gihman and Skorohod [31], p. 37–47).

Lemma 2 (see [32]). Let , be both positive and semidefinite Hermite matrix, and is a positive integer; then

We compute the th norm of the solution for (11) as follows. By Itô formula w.r.t. the function for being even, one obtains
According to Lemma 2, one has
Together with , the differential of the th norm is given by
where is an adapted process that compensates all the estimation made and could be computed explicitly.

Lemma 3. Let be even, and then there exists a constant independent of such that for .

Proof. For , define stopping time . Note that
Since for all , using and the above inequality, one concludes that integrand of the stochastic integral is bounded; hence,
Form , we get the following inequality:
Let , , then (18) becomes
which implies
Gronwall inequality implies that there exists a constant such that
By inductive method, there exist a constant such that
It remains to show that the stopping time satisfies for . By the continuity of the solution in , the norm is bounded; therefore, it converges -wise to as . Since the norm is nonnegative and bounded for all , for , Fatou Lemma implies that

Lemma 4. Let be even, and then there exists a constant independent of such that for .

Proof. From (15), it follows that
which implies
Taking to the power to obtain an expression for , one has
According to for , one obtains
Since the solution for (11) is measurable for and continuous in , the norm is -measurable (see Wentzell [33], p. 89, p. 18). Therefore, applying , together with Hölder inequality to remove the powers outside the deterministic integrals and , changing expectation and integration by Fubini theorem and using Lemma 3, one obtains
which is bounded by a constant . To further estimate the stochastic integral , using the Burkholder-Davis-Gundy (see Mao [14], p. 7), one obtains
with the constant
For , using and, for , Hölder inequality to remove the powers outside the integral on the right side of (29), afterwards, one proceeds similar to handle the Lebesgue integrals and , which leads to a constant . Combining the above discussion, one obtains
To complete the proof one needs to show for . Mentioning that the solution is continuous in , thus is bounded and converges -wise, for , to . Moreover, Fatou Lemma and (31) imply
Now, we state our first main result.

Theorem 5. Let and be satisfied, and then (7) possesses a unique almost sure continuous solution process.

Proof. It is easy to see that the uniqueness follows from the Lipschitz condition fulfilled by the coefficients (see Remark 2 in Gihman and Skorohod [31], p. 45).In the following, we only need to prove the existence of solution for (7).Note that Lemma 1 ensures the existence of a solution for (11). We firstly show that converges to a function for .Again let denote the stopping time for . From Chebyshev inequality and Lemma 4, it follows that
Note that this states slightly more than convergence in probability of . One can find, for almost every , an such that . Moreover, one has and (almost sure) on for all (see Gihman and Skorohod [14], p. 4). Thus, if , then for all . From the above discussion, it follows that the set is monotonously increasing and converges to , for . We point out once more that if , then one can express, for almost , the limit function by for all . (Actually is only a version of on ; i.e., there exists an exceptional -null set . Note that there are countable many such null sets, so that the union over all the -null set is again a null set.) Therefore, converges uniformly in to , together with is continuous in , is further continuous in .Secondly, we further show that the limit function is a real solution for (7).For , this is true because for all . For , one considers the limit function of for . According to and for , the almost sure convergence of to implies
Therefore, is a solution for (7) on . The proof is completed.

Under the assumptions and , the solution has some important properties.

Corollary 6. The solution for (7) has the following properties.(i) is a -measurable homogeneous Markov process.(ii)Let for a fixed even , and then there exists a constant such that
Furthermore, for every deterministic and bounded set , the constant is finite ( is deterministic).

Proof. (i) Because the coefficients , of (11) are independent of , respectively, and fulfill both linear growth condition and Lipschitz condition, the solution is a homogeneous Markov process by Theorem 1 in Gihman and Skorohod [31] (Section 10). Therefore, the solution of system (7) is also a homogeneous Markov process since uniformly converges to .

(ii) From the proof of Lemma 4, it is to see that there exists the constant in (ii). Note that , , can be bounded by a probably more accurate constant using Lemma 3. Every bounded set is contained in a ball of appropriate radius and center zero. Setting , the assertion follows from the linear dependence of the bounding constant (cf. (23) and (31) resp.).

Corollary 7. Let , and then there exists a constant such that

Proof. The assumptions imply that the constant in the proof of Lemma 3 is negative. Hence, there exist a bounded constant only depending on the initial condition, but not on .

4. Applications

In this section, we prove the existence and uniqueness of solution for the stochastic Lorenz system for weather forecasting as an application to illustrate the effectiveness of our results.

The Lorenz system was introduced by Lorenz [34]. For the physical meaning of the system, the reader is referred to Peitgen et al. [35]. Forces not described by those equations are assumed to be random. We model these influences by white noise. This leads to the stochastic Lorenz system which has been described in detail in Arnold [36] and has been intensively studied in [6, 37] and so forth.

Definition 8. Let , be constants, then the stochastic Lorenz system is defined by
the two parts of the drift are given by
and the noise term matrix satisfies Lipschitz as well as linear growth condition.

Remark 9. If one considers the noise to act in Stratonovich form, one talks about the Stratonovich Lorenz system. Note that the Stratonovich and the corresponding Itô Lorenz system are equivalent up to an additional drift term (see Arnold [38], p. 181).

Proof. Let , , , and , and one has
The three parts of the drift are given by
Obviously, the noise term matrix remains satisfying Lipschitz as well as linear growth condition. According to (40), one has . Moreover,
where . Hence according to Theorem 10, system (39) exists a unique solution, so is system (37).

5. Conclusion

This paper considers the problems of existence and uniqueness of solution for a class of stochastic differential equations whose nonlinear part does not satisfy linear growth condition. Some new criteria ensuring the existence and uniqueness of solution were presented. These criteria extend, improve, complement a number of results about the existence and uniqueness of solution, and handle some cases not covered by known criteria. Furthermore, these criteria are important in applications such as stochastic Lorenz system for weather forecasting, some other systems, and so on.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (no. 11271139), (no. 11201089) and (no. 11301090) and Guangxi Natural Science Foundation (no. 2013GXNSFAA019014) and (no. 2013GXNSFBA019016).